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MegaAgent: A Large-Scale Autonomous LLM-based Multi-Agent System Without Predefined SOPs

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arxiv 2408.09955 v3 pith:5DYB3MBW submitted 2024-08-19 cs.MA

Qian Wang , Tianyu Wang , Zhenheng Tang , Qinbin Li , Nuo Chen , Jingsheng Liang , Bingsheng He This is my paper · ORCID
classification cs.MA
keywords megaagentagentsllm-basedmulti-agentpredefinedsopssystemtask
verification ladder T0 review T1 audit T2 compute T3 formal
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LLM-based multi-agent systems (MAS) have shown promise in tackling complex tasks. However, existing solutions often suffer from limited agent coordination and heavy reliance on predefined Standard Operating Procedures (SOPs), which demand extensive human input. To address these limitations, we propose MegaAgent, a large-scale autonomous LLM-based multi-agent system. MegaAgent generates agents based on task complexity and enables dynamic task decomposition, parallel execution, efficient communication, and comprehensive system monitoring of agents. In evaluations, MegaAgent demonstrates exceptional performance, successfully developing a Gobang game within 800 seconds and scaling up to 590 agents in a national policy simulation to generate multi-domain policies. It significantly outperforms existing systems, such as MetaGPT, in both task completion efficiency and scalability. By eliminating the need for predefined SOPs, MegaAgent demonstrates exceptional scalability and autonomy, setting a foundation for advancing true autonomy in MAS. Our code is available at https://github.com/Xtra-Computing/MegaAgent .

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Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Chain-of-Models: Cross-Model Auditing for Bias-Robust LLM Judges

    cs.CL 2026-05 conditional novelty 6.0 of 10

    Per-bias selection of a cross-family LLM auditor lifts biased-judgment accuracy from 0.805/0.824 baselines to 0.884.

  2. CodeAgents: A Token-Efficient Framework for Codified Multi-Agent Reasoning in LLMs

    cs.AI 2025-07 conditional novelty 5.0 of 10

    Rewriting multi-agent LLM prompts as pseudocode with assertions, replanning, and comments yields moderate accuracy gains and large token savings in the tests reported here, though some headline numbers are overstated.

  3. PulseReddit: A Novel Reddit Dataset for Benchmarking MAS in High-Frequency Cryptocurrency Trading

    cs.CL 2025-06 reject novelty 5.0 of 10

    MAS traders using Reddit sentiment from PulseReddit beat traditional baselines in the reported bull-market backtests, but the gains are small, most runs lose money, and the evaluation has critical flaws.

  4. Synergizing Logical Reasoning, Knowledge Management and Collaboration in Multi-Agent LLM System

    cs.MA 2025-07 conditional novelty 4.0 of 10

    SynergyMAS combines a graph database with a Clingo logic solver, corrective RAG, and Theory of Mind prompts in a hierarchical multi-agent team, demonstrated on a Smart Home Energy Management case study.

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